Matches in SemOpenAlex for { <https://semopenalex.org/work/W2959090904> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W2959090904 abstract "This letter provides a brand new way of feature extraction, which can be applied in the supervised classification of hyperspectral image. The convolutional neural network (CNN) has been proven to be an effective method of image classification. However, due to its long training time, it requires a large amount of the labeled data to achieve the expected outcome. To decrease the training time and reduce the dependence on large labeled data set, we propose using the method of transfer learning by taking the advantage of Bayesian framework to integrate with spectrum and spatial information, making use of the Markov property of images to distinguish and separate the ones with class tags, and employing the CNN trained by band samples randomly selected from the data sets. The method of classification mentioned in our letter makes use of the real hyperspectral data sets to perform the experimental evaluation. The result demonstrates that our method is superior to the previous methods." @default.
- W2959090904 created "2019-07-23" @default.
- W2959090904 creator A5010776860 @default.
- W2959090904 creator A5028235866 @default.
- W2959090904 creator A5052904840 @default.
- W2959090904 creator A5053646157 @default.
- W2959090904 creator A5064842058 @default.
- W2959090904 date "2020-03-01" @default.
- W2959090904 modified "2023-10-12" @default.
- W2959090904 title "Hyperspectral Image Classification With Transfer Learning and Markov Random Fields" @default.
- W2959090904 cites W1521436688 @default.
- W2959090904 cites W1974981350 @default.
- W2959090904 cites W1976152398 @default.
- W2959090904 cites W2028444561 @default.
- W2959090904 cites W2116793806 @default.
- W2959090904 cites W2135253985 @default.
- W2959090904 cites W2147590979 @default.
- W2959090904 cites W2149933564 @default.
- W2959090904 cites W2158400785 @default.
- W2959090904 cites W2162854380 @default.
- W2959090904 cites W2311217941 @default.
- W2959090904 cites W2314785379 @default.
- W2959090904 cites W2464755555 @default.
- W2959090904 cites W2577238056 @default.
- W2959090904 cites W2746834958 @default.
- W2959090904 cites W2897819140 @default.
- W2959090904 cites W2902905019 @default.
- W2959090904 cites W3104795559 @default.
- W2959090904 cites W3105100264 @default.
- W2959090904 cites W3106090851 @default.
- W2959090904 cites W391985582 @default.
- W2959090904 doi "https://doi.org/10.1109/lgrs.2019.2923647" @default.
- W2959090904 hasPublicationYear "2020" @default.
- W2959090904 type Work @default.
- W2959090904 sameAs 2959090904 @default.
- W2959090904 citedByCount "12" @default.
- W2959090904 countsByYear W29590909042020 @default.
- W2959090904 countsByYear W29590909042021 @default.
- W2959090904 countsByYear W29590909042022 @default.
- W2959090904 countsByYear W29590909042023 @default.
- W2959090904 crossrefType "journal-article" @default.
- W2959090904 hasAuthorship W2959090904A5010776860 @default.
- W2959090904 hasAuthorship W2959090904A5028235866 @default.
- W2959090904 hasAuthorship W2959090904A5052904840 @default.
- W2959090904 hasAuthorship W2959090904A5053646157 @default.
- W2959090904 hasAuthorship W2959090904A5064842058 @default.
- W2959090904 hasConcept C115961682 @default.
- W2959090904 hasConcept C119857082 @default.
- W2959090904 hasConcept C150899416 @default.
- W2959090904 hasConcept C153180895 @default.
- W2959090904 hasConcept C154945302 @default.
- W2959090904 hasConcept C159078339 @default.
- W2959090904 hasConcept C177264268 @default.
- W2959090904 hasConcept C199360897 @default.
- W2959090904 hasConcept C2777212361 @default.
- W2959090904 hasConcept C41008148 @default.
- W2959090904 hasConcept C52622490 @default.
- W2959090904 hasConcept C58489278 @default.
- W2959090904 hasConcept C75294576 @default.
- W2959090904 hasConcept C81363708 @default.
- W2959090904 hasConceptScore W2959090904C115961682 @default.
- W2959090904 hasConceptScore W2959090904C119857082 @default.
- W2959090904 hasConceptScore W2959090904C150899416 @default.
- W2959090904 hasConceptScore W2959090904C153180895 @default.
- W2959090904 hasConceptScore W2959090904C154945302 @default.
- W2959090904 hasConceptScore W2959090904C159078339 @default.
- W2959090904 hasConceptScore W2959090904C177264268 @default.
- W2959090904 hasConceptScore W2959090904C199360897 @default.
- W2959090904 hasConceptScore W2959090904C2777212361 @default.
- W2959090904 hasConceptScore W2959090904C41008148 @default.
- W2959090904 hasConceptScore W2959090904C52622490 @default.
- W2959090904 hasConceptScore W2959090904C58489278 @default.
- W2959090904 hasConceptScore W2959090904C75294576 @default.
- W2959090904 hasConceptScore W2959090904C81363708 @default.
- W2959090904 hasFunder F4320321001 @default.
- W2959090904 hasLocation W29590909041 @default.
- W2959090904 hasOpenAccess W2959090904 @default.
- W2959090904 hasPrimaryLocation W29590909041 @default.
- W2959090904 hasRelatedWork W1000462 @default.
- W2959090904 hasRelatedWork W13815759 @default.
- W2959090904 hasRelatedWork W1383942 @default.
- W2959090904 hasRelatedWork W2873872 @default.
- W2959090904 hasRelatedWork W2988963 @default.
- W2959090904 hasRelatedWork W4275953 @default.
- W2959090904 hasRelatedWork W7811848 @default.
- W2959090904 hasRelatedWork W8261557 @default.
- W2959090904 hasRelatedWork W9362070 @default.
- W2959090904 hasRelatedWork W9402503 @default.
- W2959090904 isParatext "false" @default.
- W2959090904 isRetracted "false" @default.
- W2959090904 magId "2959090904" @default.
- W2959090904 workType "article" @default.